In this research we look into users who had the overdraft product enabled in H1 2022, and understand how they are using the product, by splitting them into usage groups, and understanding how these groups transact while using this product.
These groups are split into 2 usage types:
Having these groups in mind, we will be answering the following questions:
Here we can see that 39.4% of users with overdraft enabled in H2 2022 didn't use the product. For the ones that did use overdraft in this timeframe, we can see that our group split led to roughly even groups.
| days_bucket | n_users | perc_users | |
|---|---|---|---|
| 0 | not using od | 58332 | 39.4 |
| 1 | <=10 | 27989 | 18.1 |
| 2 | >=11 and <= 27 | 33344 | 21.1 |
| 3 | >=28 | 30594 | 21.4 |
As for the usage groups, we can see that 23.4% of all users are using overdraft below up to 20% of their limit. Since we are particularly interested in exploring high overdraft usage, we split the 80%+ group in intervals of 10% instead of 20%, so we can have a more detailed view on the >90% usage users, who correspond to 8.4% of all users.
| usage_buckets | n_users | perc_users | |
|---|---|---|---|
| 0 | 0% | 58332 | 39.4 |
| 1 | <=20% | 34960 | 23.4 |
| 2 | 20% to 40% | 12753 | 8.4 |
| 3 | 40% to 60% | 10815 | 7.1 |
| 4 | 60% to 80% | 11265 | 7.3 |
| 5 | 80% to 90% | 7589 | 5.0 |
| 6 | > 90% | 12594 | 8.4 |
| 7 | in arrears | 1951 | 1.1 |
Here we can see that the number of monthly days per group is relatively stable in the 6 months we have looked into, with a small decrease in February, which is of course due to this month being shorter than the remaining ones.
We can also see a similarly stable pattern for the usage buckets.
As for rating classes, we can see an interesting shift after April 2022. This corresponds to the launch of the 2.0 version of the service that originates these scores (Lisbon). With this new version, there is a bigger difference between these groups and the >=28 days group has an average very close to 12, the cut-off score for eligibility.
We see a similar polarizing effect after the Lisbon 2.0 launch when it comes to usage buckets. This time around, we can see that on average 80%+ usage users are above 12, meaning once again that on average these high intensity users would no longer be eligible for the product after the Lisbon 2.0 Launch.
There are a few transformations we decided to do in order to make this metric meaningful. First, as for transaction categories, we started from the transaction types ( here you can find the details for each of those types). Since a lot of the big outgoing transaction groups such as Card Presentments (PTs) and Direct Transfers didn't give us much detail, we added more granularity to these:
Also, in order to exclude outliers where users have only one or two transactions corresponding to very big percentages in their transaction proportions, we're excluding users with less than 6 transactions in the selected 6 month period. This leads to an 11.9% decrease in the user base described above (from 150.3k users to 132.4k users).
Then, in order to understand the impact of each transaction category in user spending, we calculated the percentage of each transaction category for each user (100% being the total outgoing transactions), and then calculated the average for all users in each group. We split these results into 2 dimensions: number of transactions and transaction volume.
Note: For each of the charts below, we only see the top 10 transactions per group. Whenever we see empty transaction categories there, it doesn't mean we don't have transactions there, it just means that they didn't make it to the top 10 for that group.
When it comes to the number of transactions, we can see that:
As for the transaction volume, we see the following patterns:
When it comes to the number of transactions, we can see some interesting patterns that split users that use none or less than 90% of their overdraft limit, and the ones that use more than 90% are in arrears:
When looking at the transaction volumes, we see some different patterns:
Here we apply the same logic as above, but only for incoming transactions (e.g. considering Credit Transfers instead of Direct Transfers and Direct Debits).
When it comes to the number of incoming transactions, we can see 2 main patterns:
As for the transaction volume we can see:
When looking at number of transactions for usage groups, the following patterns emerge:
And finally, we can see the following for the transaction volumes:
| mcc_category | merchant_name | sum_n_txns | n_users | rn | |
|---|---|---|---|---|---|
| 0 | gambling_gaming | WIN2DAY | 15484 | 824 | 1 |
| 1 | gambling_gaming | TIPICO | 14058 | 866 | 2 |
| 2 | gambling_gaming | LOTTOLAND | 9189 | 181 | 3 |
| 3 | gambling_gaming | POKERSTARS | 8022 | 672 | 4 |
| 4 | gambling_gaming | Red Rhino Limited | 7954 | 442 | 5 |
| 5 | gambling_gaming | BET365 | 7693 | 455 | 6 |
| 6 | gambling_gaming | BWIN.DE | 7641 | 364 | 7 |
| 7 | gambling_gaming | Lotto24 | 7478 | 795 | 8 |
| 8 | gambling_gaming | ROL*Wunderino | 4019 | 280 | 9 |
| 9 | gambling_gaming | PAYPAL *HILLSIDESPO | 3516 | 167 | 10 |
| 10 | money_cash_financial | Wise | 27058 | 5460 | 1 |
| 11 | money_cash_financial | Binance | 17302 | 3772 | 2 |
| 12 | money_cash_financial | CRO | 13399 | 2094 | 3 |
| 13 | money_cash_financial | CRO Topup | 12808 | 1021 | 4 |
| 14 | money_cash_financial | COINBASE IRELAND | 8511 | 1619 | 5 |
| 15 | money_cash_financial | CB PAYMENTS EUR | 7368 | 1696 | 6 |
| 16 | money_cash_financial | Taptap Send BE | 5749 | 356 | 7 |
| 17 | money_cash_financial | binance.com | 5315 | 1641 | 8 |
| 18 | money_cash_financial | PAYPAL *HVV | 3679 | 620 | 9 |
| 19 | money_cash_financial | crypto.com | 3542 | 984 | 10 |